Automated visual inspection and defect detection of large-scale silicon strip sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract For the Phase-II Upgrade of the ATLAS Detector, the Inner Detector will be replaced with the Inner Tracker (ITk), consisting of a pixel and a strip tracker. The 17,888 silicon strip detector modules comprising the ITk strip tracker will be assembled from silicon strip sensors and flexes with readout chips in a manual assembly process performed at 20 module assembly sites in a complex distribution chain, which requires quality control steps to be performed after each distribution and assembly step. Sensor quality control requires a visual inspection of the full sensor area (about 100 cm 2 ) of each sensor to detect and log any defects (e.g. scratches, breakdown areas or chipped corners) or contamination. Since manual surveys of full sensor areas for several thousand sensors are both time-consuming and prone to errors, alternative methods were investigated to automate the process and improve its reliability. This paper presents a setup developed to take high-resolution images of full silicon strip sensors with high repeatability quickly and an algorithm developed for the automated detection of defects, built using functions and filters from popular open-source visual processing packages OpenCV and Scikit-image. Methods were developed both for small-scale high-resolution images and full-size sensor images with lower resolution — both are presented here.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it